{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# 定义数据处理方法\n",
    "def preprocess_dataset(data_frame):\n",
    "    # 先复制产生一个新的数据集，这样我们的原始数据集不会被修改\n",
    "    data_frame = data_frame.copy()\n",
    "\n",
    "    # 丢弃缺失年龄，性别和登船口岸信息的数据\n",
    "    data_frame = data_frame.dropna(subset=['Age', 'Sex', 'Embarked', 'Fare'])\n",
    "\n",
    "    # 把性别从 male, female 转换成 0 和 1\n",
    "    data_frame.Sex = data_frame.Sex.replace(['male', 'female'], value=[0, 1])\n",
    "\n",
    "    # 把登船口岸从 S, C, Q 转换成 0, 1, 2\n",
    "    data_frame.Embarked = data_frame.Embarked.replace(['S', 'C', 'Q'], value=[0, 1, 2])\n",
    "\n",
    "    # 丢弃我们不需要的字段\n",
    "    data_frame = data_frame.drop(columns=['Name', 'Ticket', 'Cabin', 'PassengerId'])\n",
    "    return data_frame\n",
    "\n",
    "# 读取和处理训练数据数据\n",
    "df = pd.read_csv('data/titanic/train.csv')\n",
    "train_df = preprocess_dataset(df)\n",
    "\n",
    "# 拆分特征和标签\n",
    "train_labels = train_df.pop('Survived')\n",
    "\n",
    "# 定义模型\n",
    "from tensorflow.python import keras\n",
    "\n",
    "# 使用 L 代表 keras.layers ，方便后续调用\n",
    "L = keras.layers\n",
    "\n",
    "model = keras.Sequential([\n",
    "    # 添加一个包含 12 个神经元的全连接层，输入维度为 7，输出维度 12\n",
    "    L.Dense(12, input_dim=7, activation='relu', name='input_layer'),\n",
    "    # 添加一个包含 6 个神经元的全连接层，上层的输出为本层的输入\n",
    "    L.Dense(6, activation='relu', name='hidden_layer'),\n",
    "    # 添加一个包含 1 个神经元的全连接层，使用 sigmoid 函数来确保网络输出在 0 和 1 之间\n",
    "    L.Dense(1, activation='sigmoid', name='output_layer')\n",
    "])\n",
    "\n",
    "# 编译模型\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 模型保存和恢复\n",
    "### 6.2.1 全模型保存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_layer (Dense)          (None, 12)                96        \n",
      "_________________________________________________________________\n",
      "hidden_layer (Dense)         (None, 6)                 78        \n",
      "_________________________________________________________________\n",
      "output_layer (Dense)         (None, 1)                 7         \n",
      "=================================================================\n",
      "Total params: 181\n",
      "Trainable params: 181\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import os\n",
    "# 创建目录\n",
    "os.makedirs('outputs/chapter6', exist_ok=True)\n",
    "\n",
    "# 保存模型\n",
    "model.save('outputs/chapter6/my_model.h5')\n",
    "\n",
    "# 使用保存的文件恢复模型，此时不需要任何之前的代码，只需要这个文件\n",
    "new_model = tf.keras.models.load_model('outputs/chapter6/my_model.h5')\n",
    "new_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2.2 保存为 SavedModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: outputs/chapter6/saved_model/assets\n",
      "\u001B[1m\u001B[36massets\u001B[m\u001B[m         saved_model.pb \u001B[1m\u001B[36mvariables\u001B[m\u001B[m\n"
     ]
    }
   ],
   "source": [
    "# 保存模型为 SavedModel 格式\n",
    "tf.saved_model.save(model, 'outputs/chapter6/saved_model')\n",
    "# 加载 SavedModel 模型为 Keras 模型\n",
    "new_model = tf.keras.models.load_model('outputs/chapter6/saved_model')\n",
    "!ls outputs/chapter6/saved_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2.3 仅保存模型结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取模型配置，config 是个 python 字典。\n",
    "config = model.get_config()\n",
    "\n",
    "# 使用配置字典重新初始化模型\n",
    "reinitialized_model = keras.Sequential.from_config(config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2.4 仅保存模型权重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = model.get_weights()  # 获取模型的权重\n",
    "model.set_weights(weights)     # 为模型设定权重\n",
    "\n",
    "# 把权重保存到磁盘\n",
    "model.save_weights('outputs/chapter6/model_weights.h5')\n",
    "# 从磁盘加载模型权重\n",
    "model.load_weights('outputs/chapter6/model_weights.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.3 模型增量更新"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 10 samples\n",
      "10/10 [==============================] - 0s 43ms/sample - loss: 0.7486 - accuracy: 0.4000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x141a50438>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加载保存模型\n",
    "new_model = tf.keras.models.load_model('outputs/chapter6/my_model.h5')\n",
    "# 或加载 SavedModel 格式模型\n",
    "new_model = tf.keras.models.load_model('outputs/chapter6/saved_model')\n",
    "\n",
    "# 假数据\n",
    "new_x = np.random.random((10,7))\n",
    "new_y = np.random.randint(0, 1, 10)\n",
    "\n",
    "# 继续调用 fit 训练即可\n",
    "new_model.fit(new_x, new_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.4 训练回调\n",
    "### 6.4.1 模型检查点和提前终止"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Falling back from v2 loop because of error: Failed to find data adapter that can handle input: <class 'pandas.core.frame.DataFrame'>, <class 'NoneType'>\n",
      "Train on 712 samples, validate on 712 samples\n",
      "Epoch 1/40\n",
      "712/712 [==============================] - 0s 119us/sample - loss: 0.6134 - accuracy: 0.6643 - val_loss: 0.5987 - val_accuracy: 0.6671\n",
      "Epoch 2/40\n",
      "712/712 [==============================] - 0s 87us/sample - loss: 0.5976 - accuracy: 0.6643 - val_loss: 0.5894 - val_accuracy: 0.6812\n",
      "Epoch 3/40\n",
      "712/712 [==============================] - 0s 71us/sample - loss: 0.5889 - accuracy: 0.6910 - val_loss: 0.5838 - val_accuracy: 0.6812\n",
      "Epoch 4/40\n",
      "712/712 [==============================] - 0s 113us/sample - loss: 0.5817 - accuracy: 0.6812 - val_loss: 0.5672 - val_accuracy: 0.6854\n",
      "Epoch 5/40\n",
      "712/712 [==============================] - 0s 88us/sample - loss: 0.5855 - accuracy: 0.6854 - val_loss: 0.5606 - val_accuracy: 0.7079\n",
      "Epoch 6/40\n",
      "712/712 [==============================] - 0s 83us/sample - loss: 0.5705 - accuracy: 0.7037 - val_loss: 0.5882 - val_accuracy: 0.7374\n",
      "Epoch 7/40\n",
      "712/712 [==============================] - 0s 68us/sample - loss: 0.5747 - accuracy: 0.7065 - val_loss: 0.5458 - val_accuracy: 0.7247\n",
      "Epoch 8/40\n",
      "712/712 [==============================] - 0s 66us/sample - loss: 0.5527 - accuracy: 0.7261 - val_loss: 0.5401 - val_accuracy: 0.7261\n",
      "Epoch 9/40\n",
      "712/712 [==============================] - 0s 92us/sample - loss: 0.5449 - accuracy: 0.7289 - val_loss: 0.5317 - val_accuracy: 0.7486\n",
      "Epoch 10/40\n",
      "712/712 [==============================] - 0s 77us/sample - loss: 0.5322 - accuracy: 0.7416 - val_loss: 0.5256 - val_accuracy: 0.7458\n",
      "Epoch 11/40\n",
      "712/712 [==============================] - 0s 70us/sample - loss: 0.5280 - accuracy: 0.7430 - val_loss: 0.5198 - val_accuracy: 0.7444\n",
      "Epoch 12/40\n",
      "712/712 [==============================] - 0s 83us/sample - loss: 0.5246 - accuracy: 0.7388 - val_loss: 0.5163 - val_accuracy: 0.7528\n",
      "Epoch 13/40\n",
      "712/712 [==============================] - 0s 73us/sample - loss: 0.5175 - accuracy: 0.7612 - val_loss: 0.5162 - val_accuracy: 0.7486\n",
      "Epoch 14/40\n",
      "712/712 [==============================] - 0s 87us/sample - loss: 0.5199 - accuracy: 0.7402 - val_loss: 0.5061 - val_accuracy: 0.7556\n",
      "Epoch 15/40\n",
      "712/712 [==============================] - 0s 81us/sample - loss: 0.5071 - accuracy: 0.7711 - val_loss: 0.5138 - val_accuracy: 0.7669\n",
      "Epoch 16/40\n",
      "712/712 [==============================] - 0s 91us/sample - loss: 0.5171 - accuracy: 0.7626 - val_loss: 0.5354 - val_accuracy: 0.7795\n",
      "Epoch 17/40\n",
      "712/712 [==============================] - 0s 72us/sample - loss: 0.5242 - accuracy: 0.7430 - val_loss: 0.5177 - val_accuracy: 0.7739\n",
      "Epoch 18/40\n",
      "712/712 [==============================] - 0s 65us/sample - loss: 0.5210 - accuracy: 0.7444 - val_loss: 0.4917 - val_accuracy: 0.7711\n",
      "Epoch 19/40\n",
      "712/712 [==============================] - 0s 66us/sample - loss: 0.4929 - accuracy: 0.7683 - val_loss: 0.4996 - val_accuracy: 0.7795\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x143a36240>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "# 当被监测的指标不再提升，则停止训练。\n",
    "early_stop = tf.keras.callbacks.EarlyStopping(\n",
    "    monitor='val_accuracy',  # 被监测的指标，这里监控模型验证集准确度\n",
    "    patience=3)              # 如果指标在多余三轮的时间（即四轮）不变后，中断训练\n",
    "\n",
    "# 在每个训练期之后保存模型。\n",
    "model_path = 'outputs/chapter6/best_model.h5'\n",
    "model_checkpoint = tf.keras.callbacks.ModelCheckpoint(\n",
    "    filepath=model_path,    # 模型存储路径\n",
    "    monitor='val_accuracy', # 被监测的指标，这里监控模型验证集准确度\n",
    "    save_best_only=True)    # 只有指标被改善时候存储，如果为 False，则每一轮保存\n",
    "\n",
    "model.fit(train_df.values,\n",
    "          train_labels.values,\n",
    "          validation_data=(train_df.values, train_labels.values),\n",
    "          epochs=40,\n",
    "          callbacks=[early_stop, model_checkpoint])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.4.2 动态调整学习率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 712 samples, validate on 712 samples\n",
      "Epoch 1/40\n",
      "712/712 [==============================] - 0s 539us/sample - loss: 0.4412 - accuracy: 0.8034 - val_loss: 0.4306 - val_accuracy: 0.7949\n",
      "Epoch 2/40\n",
      "712/712 [==============================] - 0s 96us/sample - loss: 0.4378 - accuracy: 0.7978 - val_loss: 0.4429 - val_accuracy: 0.7935\n",
      "Epoch 3/40\n",
      "712/712 [==============================] - 0s 90us/sample - loss: 0.4455 - accuracy: 0.7978 - val_loss: 0.4282 - val_accuracy: 0.8118\n",
      "Epoch 4/40\n",
      "712/712 [==============================] - 0s 94us/sample - loss: 0.4372 - accuracy: 0.8118 - val_loss: 0.4284 - val_accuracy: 0.8006\n",
      "Epoch 5/40\n",
      "712/712 [==============================] - 0s 94us/sample - loss: 0.4862 - accuracy: 0.7879 - val_loss: 0.4357 - val_accuracy: 0.8104\n",
      "Epoch 6/40\n",
      "712/712 [==============================] - 0s 93us/sample - loss: 0.4591 - accuracy: 0.8034 - val_loss: 0.4300 - val_accuracy: 0.8216\n",
      "Epoch 7/40\n",
      "712/712 [==============================] - 0s 89us/sample - loss: 0.4518 - accuracy: 0.8062 - val_loss: 0.4488 - val_accuracy: 0.8132\n",
      "Epoch 8/40\n",
      "712/712 [==============================] - 0s 92us/sample - loss: 0.4445 - accuracy: 0.8062 - val_loss: 0.4534 - val_accuracy: 0.8076\n",
      "Epoch 9/40\n",
      "712/712 [==============================] - 0s 94us/sample - loss: 0.4555 - accuracy: 0.8048 - val_loss: 0.4305 - val_accuracy: 0.8216\n",
      "Epoch 10/40\n",
      "712/712 [==============================] - 0s 91us/sample - loss: 0.4386 - accuracy: 0.8118 - val_loss: 0.4276 - val_accuracy: 0.8034\n",
      "Epoch 11/40\n",
      "712/712 [==============================] - 0s 98us/sample - loss: 0.4359 - accuracy: 0.8020 - val_loss: 0.4300 - val_accuracy: 0.8230\n",
      "Epoch 12/40\n",
      "712/712 [==============================] - 0s 100us/sample - loss: 0.4374 - accuracy: 0.7978 - val_loss: 0.4336 - val_accuracy: 0.8034\n",
      "Epoch 13/40\n",
      "712/712 [==============================] - 0s 98us/sample - loss: 0.4415 - accuracy: 0.7992 - val_loss: 0.4285 - val_accuracy: 0.8160\n",
      "Epoch 14/40\n",
      "712/712 [==============================] - 0s 111us/sample - loss: 0.4299 - accuracy: 0.8048 - val_loss: 0.4258 - val_accuracy: 0.8132\n",
      "Epoch 15/40\n",
      "712/712 [==============================] - 0s 105us/sample - loss: 0.4343 - accuracy: 0.8090 - val_loss: 0.4265 - val_accuracy: 0.8132\n",
      "Epoch 16/40\n",
      "712/712 [==============================] - 0s 97us/sample - loss: 0.4363 - accuracy: 0.8090 - val_loss: 0.4288 - val_accuracy: 0.8244\n",
      "Epoch 17/40\n",
      "712/712 [==============================] - 0s 94us/sample - loss: 0.4303 - accuracy: 0.8090 - val_loss: 0.4260 - val_accuracy: 0.8006\n",
      "Epoch 18/40\n",
      "712/712 [==============================] - 0s 94us/sample - loss: 0.4330 - accuracy: 0.8020 - val_loss: 0.4246 - val_accuracy: 0.8104\n",
      "Epoch 19/40\n",
      "712/712 [==============================] - 0s 91us/sample - loss: 0.4225 - accuracy: 0.8146 - val_loss: 0.4608 - val_accuracy: 0.7921\n",
      "Epoch 20/40\n",
      "712/712 [==============================] - 0s 92us/sample - loss: 0.4369 - accuracy: 0.8048 - val_loss: 0.4305 - val_accuracy: 0.8174\n",
      "Epoch 21/40\n",
      "712/712 [==============================] - 0s 91us/sample - loss: 0.4337 - accuracy: 0.8062 - val_loss: 0.4241 - val_accuracy: 0.8132\n",
      "Epoch 22/40\n",
      "712/712 [==============================] - 0s 89us/sample - loss: 0.4384 - accuracy: 0.8006 - val_loss: 0.4345 - val_accuracy: 0.7963\n",
      "Epoch 23/40\n",
      "712/712 [==============================] - 0s 93us/sample - loss: 0.4404 - accuracy: 0.8034 - val_loss: 0.4273 - val_accuracy: 0.7949\n",
      "Epoch 24/40\n",
      "712/712 [==============================] - 0s 90us/sample - loss: 0.4436 - accuracy: 0.7963 - val_loss: 0.4310 - val_accuracy: 0.8202\n",
      "Epoch 25/40\n",
      "712/712 [==============================] - 0s 119us/sample - loss: 0.4316 - accuracy: 0.8104 - val_loss: 0.4356 - val_accuracy: 0.7935\n",
      "Epoch 26/40\n",
      "712/712 [==============================] - 0s 103us/sample - loss: 0.4334 - accuracy: 0.8048 - val_loss: 0.4250 - val_accuracy: 0.8048\n",
      "Epoch 27/40\n",
      "712/712 [==============================] - 0s 116us/sample - loss: 0.4347 - accuracy: 0.7963 - val_loss: 0.4360 - val_accuracy: 0.7992\n",
      "Epoch 28/40\n",
      "712/712 [==============================] - 0s 115us/sample - loss: 0.4355 - accuracy: 0.8020 - val_loss: 0.4260 - val_accuracy: 0.8104\n",
      "Epoch 29/40\n",
      "712/712 [==============================] - 0s 135us/sample - loss: 0.4313 - accuracy: 0.8118 - val_loss: 0.4244 - val_accuracy: 0.8076\n",
      "Epoch 30/40\n",
      "712/712 [==============================] - 0s 116us/sample - loss: 0.4326 - accuracy: 0.8020 - val_loss: 0.4274 - val_accuracy: 0.8118\n",
      "Epoch 31/40\n",
      "712/712 [==============================] - 0s 110us/sample - loss: 0.4383 - accuracy: 0.8020 - val_loss: 0.4257 - val_accuracy: 0.8104\n",
      "Epoch 32/40\n",
      "712/712 [==============================] - 0s 98us/sample - loss: 0.4229 - accuracy: 0.8090 - val_loss: 0.4247 - val_accuracy: 0.7949\n",
      "Epoch 33/40\n",
      "712/712 [==============================] - 0s 114us/sample - loss: 0.4256 - accuracy: 0.8006 - val_loss: 0.4231 - val_accuracy: 0.8118\n",
      "Epoch 34/40\n",
      "712/712 [==============================] - 0s 101us/sample - loss: 0.4252 - accuracy: 0.8048 - val_loss: 0.4237 - val_accuracy: 0.8006\n",
      "Epoch 35/40\n",
      "712/712 [==============================] - 0s 96us/sample - loss: 0.4237 - accuracy: 0.8090 - val_loss: 0.4227 - val_accuracy: 0.8132\n",
      "Epoch 36/40\n",
      "712/712 [==============================] - 0s 90us/sample - loss: 0.4258 - accuracy: 0.8062 - val_loss: 0.4230 - val_accuracy: 0.8104\n",
      "Epoch 37/40\n",
      "712/712 [==============================] - 0s 91us/sample - loss: 0.4237 - accuracy: 0.8146 - val_loss: 0.4226 - val_accuracy: 0.8146\n",
      "Epoch 38/40\n",
      "712/712 [==============================] - 0s 102us/sample - loss: 0.4243 - accuracy: 0.8132 - val_loss: 0.4227 - val_accuracy: 0.8104\n",
      "Epoch 39/40\n",
      "712/712 [==============================] - 0s 94us/sample - loss: 0.4246 - accuracy: 0.8076 - val_loss: 0.4226 - val_accuracy: 0.8118\n",
      "Epoch 40/40\n",
      "712/712 [==============================] - 0s 93us/sample - loss: 0.4239 - accuracy: 0.8132 - val_loss: 0.4228 - val_accuracy: 0.8090\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x143fcf860>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduce_lr_callback = tf.keras.callbacks.ReduceLROnPlateau(\n",
    "    monitor = 'val_loss', # 监控模型的损失\n",
    "    factor = 0.2,         # 触发时将学习率除以 5\n",
    "    patience = 10)        # 如果验证损失在 10 轮内都没有改善，那么就触发这个回调函数\n",
    "\n",
    "model.fit(train_df.values,\n",
    "          train_labels.values,\n",
    "          validation_data=(train_df.values, train_labels.values),\n",
    "          epochs=40,\n",
    "          callbacks=[reduce_lr_callback])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.4.3 自定义回调函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Falling back from v2 loop because of error: Failed to find data adapter that can handle input: <class 'pandas.core.frame.DataFrame'>, <class 'NoneType'>\n",
      "Train on 712 samples\n",
      "Epoch 1/10\n",
      "712/712 [==============================] - 0s 48us/sample - loss: 0.4237 - accuracy: 0.8104\n",
      "Epoch 2/10\n",
      "712/712 [==============================] - 0s 71us/sample - loss: 0.4233 - accuracy: 0.8118\n",
      "Epoch 3/10\n",
      "712/712 [==============================] - 0s 45us/sample - loss: 0.4234 - accuracy: 0.8090\n",
      "Epoch 4/10\n",
      "712/712 [==============================] - 0s 40us/sample - loss: 0.4243 - accuracy: 0.8118\n",
      "Epoch 5/10\n",
      "712/712 [==============================] - 0s 46us/sample - loss: 0.4236 - accuracy: 0.8104\n",
      "Epoch 6/10\n",
      "712/712 [==============================] - 0s 46us/sample - loss: 0.4243 - accuracy: 0.8076\n",
      "Epoch 7/10\n",
      "712/712 [==============================] - 0s 66us/sample - loss: 0.4232 - accuracy: 0.8104\n",
      "Epoch 8/10\n",
      "712/712 [==============================] - 0s 49us/sample - loss: 0.4240 - accuracy: 0.8160\n",
      "Epoch 9/10\n",
      "712/712 [==============================] - 0s 41us/sample - loss: 0.4235 - accuracy: 0.8104\n",
      "Epoch 10/10\n",
      "712/712 [==============================] - 0s 52us/sample - loss: 0.4232 - accuracy: 0.8118\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x14579c9b0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class LossHistory(keras.callbacks.Callback):\n",
    "    def on_train_begin(self, logs={}):\n",
    "        # 训练开始时定义一个数组来保存数据\n",
    "        self.losses = []\n",
    "\n",
    "    def on_batch_end(self, batch, logs={}):\n",
    "        # 把每一轮 batch 的 loss 记录到数组中\n",
    "        self.losses.append(logs.get('loss'))\n",
    "\n",
    "\n",
    "loss_history_callback = LossHistory()\n",
    "\n",
    "model.fit(train_df,\n",
    "          train_labels,\n",
    "          epochs=10,\n",
    "          callbacks=[loss_history_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 设置 plt 的分辨率，默认分辨率比较低，导致图标不清晰\n",
    "plt.rcParams['figure.dpi'] = 180\n",
    "\n",
    "plt.figure()\n",
    "# 只传入一个参数的话, 默认为 y 轴, x 轴默认为 range(n)\n",
    "plt.plot(loss_history_callback.losses)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.5 Tensorboard 可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Falling back from v2 loop because of error: Failed to find data adapter that can handle input: <class 'pandas.core.frame.DataFrame'>, <class 'NoneType'>\n",
      "Train on 712 samples\n",
      "Epoch 1/20\n",
      "712/712 [==============================] - 0s 64us/sample - loss: 0.4232 - accuracy: 0.8090\n",
      "Epoch 2/20\n",
      "712/712 [==============================] - 0s 42us/sample - loss: 0.4226 - accuracy: 0.8132\n",
      "Epoch 3/20\n",
      "712/712 [==============================] - 0s 49us/sample - loss: 0.4222 - accuracy: 0.8146\n",
      "Epoch 4/20\n",
      "712/712 [==============================] - 0s 45us/sample - loss: 0.4257 - accuracy: 0.7978\n",
      "Epoch 5/20\n",
      "712/712 [==============================] - 0s 59us/sample - loss: 0.4242 - accuracy: 0.8160\n",
      "Epoch 6/20\n",
      "712/712 [==============================] - 0s 51us/sample - loss: 0.4246 - accuracy: 0.8006\n",
      "Epoch 7/20\n",
      "712/712 [==============================] - 0s 50us/sample - loss: 0.4221 - accuracy: 0.8132\n",
      "Epoch 8/20\n",
      "712/712 [==============================] - 0s 41us/sample - loss: 0.4224 - accuracy: 0.8090\n",
      "Epoch 9/20\n",
      "712/712 [==============================] - 0s 49us/sample - loss: 0.4233 - accuracy: 0.8104\n",
      "Epoch 10/20\n",
      "712/712 [==============================] - 0s 43us/sample - loss: 0.4238 - accuracy: 0.8188\n",
      "Epoch 11/20\n",
      "712/712 [==============================] - 0s 47us/sample - loss: 0.4225 - accuracy: 0.8062\n",
      "Epoch 12/20\n",
      "712/712 [==============================] - 0s 43us/sample - loss: 0.4227 - accuracy: 0.8146\n",
      "Epoch 13/20\n",
      "712/712 [==============================] - 0s 48us/sample - loss: 0.4255 - accuracy: 0.8006\n",
      "Epoch 14/20\n",
      "712/712 [==============================] - 0s 46us/sample - loss: 0.4244 - accuracy: 0.8118\n",
      "Epoch 15/20\n",
      "712/712 [==============================] - 0s 50us/sample - loss: 0.4229 - accuracy: 0.8090\n",
      "Epoch 16/20\n",
      "712/712 [==============================] - 0s 41us/sample - loss: 0.4225 - accuracy: 0.8104\n",
      "Epoch 17/20\n",
      "712/712 [==============================] - 0s 51us/sample - loss: 0.4248 - accuracy: 0.8020\n",
      "Epoch 18/20\n",
      "712/712 [==============================] - 0s 47us/sample - loss: 0.4238 - accuracy: 0.8132\n",
      "Epoch 19/20\n",
      "712/712 [==============================] - 0s 57us/sample - loss: 0.4231 - accuracy: 0.8146\n",
      "Epoch 20/20\n",
      "712/712 [==============================] - 0s 44us/sample - loss: 0.4227 - accuracy: 0.8048\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x145cc01d0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tensorboard = keras.callbacks.TensorBoard(\n",
    "    log_dir='outputs/chapter6/tf_board_log', # 日志输出目录\n",
    "    histogram_freq=2) # 对于模型中各个层计算激活值和模型权重直方图的频率\n",
    "\n",
    "model.fit(train_df,\n",
    "          train_labels,\n",
    "          epochs=20,\n",
    "          callbacks=[tensorboard])"
   ]
  },
  {
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